 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q4PFD9 from www.uniprot.org...
The NucPred score for your sequence is 0.86 (see score help below)
1 MSGYDNRDDYRHSSSSSHHRSRNSKHDSSSSSYRSHGYAPTRRRSRSPSS 50
51 YHSSSRSHRDHTTDPYPNGSGHDRRHDRDRNRERDRRDHDSPIEAAPSPD 100
101 GRRFHVEGAPRQGGRRRWDEGAPTPPTAAAPVHVSDPTRSSLNTQLPSRP 150
151 IVAPPGMAPGPPPSLPSSTPIVPSTGISISPEEQAKLAKKARLEAWRKEQ 200
201 AAKKALEEARQRARSIASAVAPSSQRTESFSSSSTPQTAPTSINAAGLRT 250
251 LSLRTDPSRTTAQNRSRTMMDDASESSKRIHLSRLGDLPPLDPSIDTAHI 300
301 ATTSVDAEDDDHQLGVAPPTQGSSAAAMDVDDDDEEEDPLDAFMLTVKSQ 350
351 VAQVNDDDRRKASASGGHERTQAKSKAVVLGRDDSDGEAEDQYEELDELD 400
401 RVGMATEDLLALAAKKVKKKDLVTVDHSAIDYEPFNKAFYHPPAEIQDMS 450
451 EELANQIRLEMDAITVRGRDCPKPLTKWSHCGLPASCLDVIKRLGYSAPT 500
501 PIQSQAMPAIMSGRDIIGVAKTGSGKTMAFLLPMFRHIKDQRPVEPSEGP 550
551 VGIIMTPTRELAVQIYREMRPFIKALGLRAACVYGGAPISEQIAEMKKTA 600
601 DIVVATPGRLIDLLTANSGRVTNLYRVTYLVLDEADRMFDMGFEPQVMKI 650
651 LNNIRPDRQTVLFSATFPKQMESLARKVLKNKPLEITVGGRSVVAAEIEQ 700
701 IVEVRSEDTKFHRLLEILGELYNREKDARTLIFVDRQEAADDLLKDLIRK 750
751 GYVTMSLHGGKDQVDRDETISDFKAGNVPIVTATSVAARGLDVKQLKLVI 800
801 NYDVPNHMEDYVHRAGRTGRAGQKGTCITFITPEQDRYARDIIAALKASA 850
851 AHVPPELEAMAASFKEKLAAGKAKAAGSGFGGKGLDRFELDREKTLKAQK 900
901 SAYGEADDDAKAAAAGDSSEDKAKTGAPPGASSSEDQLSKIQGMKIEIMQ 950
951 GAAPESVRDNKTLSASQEASAAAAAAAAARSKAEPEQELKEAAQLKAQEA 1000
1001 ALEAAKAHGADTTKLAAVLENIRRQANARKEAAKNSELDKHKDRKARDPD 1050
1051 ATDYHAIVPINDFPQRARWRVTNKETMRHLIESTGASITNKGVFYKEGTE 1100
1101 PQPGEPPKLQLLIESNTKSMVEDAVREIQRLLVEATQAVLEAEARNPGTT 1150
1151 GRYTVV 1156
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
What does the NucPred score mean?
| You have to decide on a NucPred score threshold. Sequences which score greater than or equal to this threshold are predicted to spend some time in the nucleus. Higher thresholds yield fewer predicted nuclear proteins, but these predictions are more accurate (you can have higher confidence in them). The table below gives more details of the performance of NucPred estimated using the sequences it was trained on (by cross-validation). Another benchmark is available in the Bioinformatics 2007 paper. |
| NucPred score threshold | Specificity | Sensitivity |
| see above | fraction of proteins predicted to be nuclear that actually are nuclear | fraction of true nuclear proteins that are predicted (coverage) |
| 0.10 | 0.45 | 0.88 |
| 0.20 | 0.52 | 0.83 |
| 0.30 | 0.57 | 0.77 |
| 0.40 | 0.63 | 0.69 |
| 0.50 | 0.70 | 0.62 |
| 0.60 | 0.71 | 0.53 |
| 0.70 | 0.81 | 0.44 |
| 0.80 | 0.84 | 0.32 |
| 0.90 | 0.88 | 0.21 |
| 1.00 | 1.00 | 0.02 |
| Sequences which score >= 0.8 with NucPred and which
are predicted by PredictNLS to contain an NLS have been shown to be 93% correct with a coverage of 16%. (PredictNLS by itself is 87% correct with 26% coverage on the same data.) |
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